This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Gibson, N. M. Articles by Olejnik, S. Search for Related Content Social Bookmarking                         What's this?

Treatment Of Missing Data At The Second Level Of Hierarchical Linear Models

Valdosta State University

Stephen Olejnik

University of Georgia

The problem of missing data at the second level of a two-level hierarchical data structure was investigated. Using data generated to simulate the 1982 High School and Beyond data set, five missing data treatments—listwise deletion, overall mean substitution, group mean substitution, the expectation maximization (EM) algorithm, and multiple imputation—were examined under four manipulated conditions: number of Level 2 variables, Level 2 sample size, Level 1 intercept-slope correlation, and percentage of missing data. Listwise deletion, group mean substitution, and the EM algorithm performed equally well for the variable having missing values. For the variables having no missing data, listwise deletion and the EM algorithm performed satisfactorily. Only listwise deletion performed well in estimating random effects except when the Level 2 sample size was 30 and 40% of the data were missing. The practical implications of the findings are discussed.

Key Words: missing data • hierarchical linear models • missing data treatments

Educational and Psychological Measurement, Vol. 63, No. 2, 204-238 (2003)
DOI: 10.1177/0013164402250987


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				M. W.-L. Cheung Comparison of Methods of Handling Missing Time-Invariant Covariates in Latent Growth Models Under the Assumption of Missing Completely at Random Organizational Research Methods, October 1, 2007; 10(4): 609 - 634. [Abstract] [PDF]